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Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning

Earthquakes can inflict significant damage to structures and infrastructures. This paper presents a machine learning model to predict ground surface deformation (GDS) induced by earthquake events. The data on historical GSD is extracted from radar product of Synthetic Aperture Radar (SAR) data of on...

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Main Authors: Usman F., Nanda, Sumantyo J.T.S.
Other Authors: 55812540000
Format: Article
Published: Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya 2023
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spelling my.uniten.dspace-267182023-05-29T17:36:19Z Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning Usman F. Nanda Sumantyo J.T.S. 55812540000 57223231920 57211868004 Earthquakes can inflict significant damage to structures and infrastructures. This paper presents a machine learning model to predict ground surface deformation (GDS) induced by earthquake events. The data on historical GSD is extracted from radar product of Synthetic Aperture Radar (SAR) data of one-year over five magnitude earthquakes that occurred within 200 kilometers of the Kota Padang Regency, West Sumatra. Building topology data of its footprint area, distance from shoreline, elevation, and coordinate were incorporated as the main features in the dataset. The earthquake parameters were taken from the USGS earthquake data catalog. Four machine learning algorithms of Neural Network (NN), Random Forest (RF), k-Nearest Neighbors (kNN), and Gradient Boosting (GB) are applied. The GSD from the trained models is predicted and compared with the measured GSD from the SAR�s product. The performances of proposed algorithms are evaluated in terms of the statistical index. A new dataset from the earthquake event in March 2022 is used to predict the GSD and further test the performance of the trained models. Overall, the four machine learning algorithms have outstanding performance, with a coefficient determinant of more than 0.9. The kNN algorithm outperforms compared to others in delineating the GSD. The trained models gave deficient prediction performance on the new dataset with a correlation coefficient of 0.228 predicted by the RF algorithm. Additional earthquake datasets and more unique features will improve the performance of the machine learning algorithms. � 2022, Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya. All rights reserved. Final 2023-05-29T09:36:19Z 2023-05-29T09:36:19Z 2022 Article 10.26554/sti.2022.7.4.435-442 2-s2.0-85140895040 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140895040&doi=10.26554%2fsti.2022.7.4.435-442&partnerID=40&md5=2fa980004b04475bed1f95cccf9b435e https://irepository.uniten.edu.my/handle/123456789/26718 7 4 435 442 All Open Access, Gold Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
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continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Earthquakes can inflict significant damage to structures and infrastructures. This paper presents a machine learning model to predict ground surface deformation (GDS) induced by earthquake events. The data on historical GSD is extracted from radar product of Synthetic Aperture Radar (SAR) data of one-year over five magnitude earthquakes that occurred within 200 kilometers of the Kota Padang Regency, West Sumatra. Building topology data of its footprint area, distance from shoreline, elevation, and coordinate were incorporated as the main features in the dataset. The earthquake parameters were taken from the USGS earthquake data catalog. Four machine learning algorithms of Neural Network (NN), Random Forest (RF), k-Nearest Neighbors (kNN), and Gradient Boosting (GB) are applied. The GSD from the trained models is predicted and compared with the measured GSD from the SAR�s product. The performances of proposed algorithms are evaluated in terms of the statistical index. A new dataset from the earthquake event in March 2022 is used to predict the GSD and further test the performance of the trained models. Overall, the four machine learning algorithms have outstanding performance, with a coefficient determinant of more than 0.9. The kNN algorithm outperforms compared to others in delineating the GSD. The trained models gave deficient prediction performance on the new dataset with a correlation coefficient of 0.228 predicted by the RF algorithm. Additional earthquake datasets and more unique features will improve the performance of the machine learning algorithms. � 2022, Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya. All rights reserved.
author2 55812540000
author_facet 55812540000
Usman F.
Nanda
Sumantyo J.T.S.
format Article
author Usman F.
Nanda
Sumantyo J.T.S.
spellingShingle Usman F.
Nanda
Sumantyo J.T.S.
Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning
author_sort Usman F.
title Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning
title_short Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning
title_full Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning
title_fullStr Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning
title_full_unstemmed Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning
title_sort prediction of ground surface deformation induced by earthquake on urban area using machine learning
publisher Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya
publishDate 2023
_version_ 1806427865324978176
score 13.211869

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